flowchart LR
A[Raw FASTQ] --> B[QC & Trimming]
B --> C[Alignment]
C --> D[Quantification]
D --> E[Differential Expression]
E --> F[Pathway Analysis]
style A fill:#e74c3c,color:white
style B fill:#f39c12,color:white
style C fill:#3498db,color:white
style D fill:#9b59b6,color:white
style E fill:#1abc9c,color:white
style F fill:#27ae60,color:white
Bulk RNA-seq Pipeline
From raw reads to differential expression and pathway analysis
Overview
This pipeline provides a comprehensive workflow for analyzing bulk RNA-sequencing data. It covers everything from quality control of raw reads to differential gene expression analysis and functional enrichment.
Pipeline Steps
1. Preprocessing & Quality Control
Quality assessment with FastQC/MultiQC and adapter trimming with fastp or Trimmomatic.
FastQC MultiQC fastp Bash
2. Read Alignment
Alignment to reference genome using STAR or HISAT2.
STAR HISAT2 SAMtools
3. Quantification
Gene-level count quantification using featureCounts or Salmon.
featureCounts Salmon R
4. Differential Expression Analysis
Statistical analysis of differential gene expression using DESeq2 or edgeR.
DESeq2 edgeR limma R
5. Pathway & Functional Analysis
Gene set enrichment analysis and pathway visualization.
clusterProfiler fgsea enrichplot R
Quick Start
# Install required packages
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("DESeq2", "clusterProfiler", "org.Hs.eg.db", "enrichplot"))
install.packages(c("tidyverse", "pheatmap", "ggrepel"))Required Inputs
| Input | Description | Format |
|---|---|---|
| Raw reads | Paired-end FASTQ files | .fastq.gz |
| Reference genome | Genome FASTA file | .fa |
| Gene annotation | GTF annotation file | .gtf |
| Sample metadata | Sample information | .csv |
Expected Outputs
- Quality control reports (HTML)
- Aligned BAM files
- Gene count matrix
- Differential expression results (CSV)
- Enrichment analysis results
- Publication-ready figures
References
- Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15:550.
- Wu T, et al. (2021). “clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.” The Innovation, 2(3):100141.